Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language by Aldi Fahluzi Muharam, Yana Aditia Gerhana, Dian Sa'adillah Maylawati, Muhammad Ali Ramdhani, Titik Khawa Abdul Rahman

“…This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for the Indonesian language. …”
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Additive Manufacturing Enabled by Electrospinning for Tougher Bio-Inspired Materials by Komal Agarwal, Yinning Zhou, Hashina Parveen Anwar Ali, Ihor Radchenko, Avinash Baji, Arief S. Budiman

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A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things by Chen Dong, Jinghui Chen, Wenzhong Guo, Jian Zou

“…After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. …”
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The Potential for a GPU-Like Overlay Architecture for FPGAs by Jeffrey Kingyens, J. Gregory Steffan

“…Through simulation of a system that (i) is programmable via NVIDIA's high-level Cg language, (ii) supports AMD's CTM r5xx GPU ISA, and (iii) is realizable on an XtremeData XD1000 FPGA-based accelerator system, we demonstrate the potential for such a system to achieve 100% utilization of a deeply pipelined floating-point datapath.…”
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FPGA Acceleration of Communication-Bound Streaming Applications: Architecture Modeling and a 3D Image Compositing Case Study by Tobias Schumacher, Tim Süß, Christian Plessl, Marco Platzner

“…We provide a characterization of the memory resources available on the XtremeData XD1000 reconfigurable computer. Based on this data, we present as a case study the implementation of a 3D image compositing accelerator that is able to double the frame rate of a parallel renderer.…”
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Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning by Yuanzhi Sun, Guangbao Mi, Peijie Li, Liangju He

“…The results show that the two algorithms based on multiple learners, gradient boosting decision tree (GBDT) and eXtreme Gradient Boosting (XGBoost), show better performance. …”
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Machine learning algorithms can predict emotional valence across ungulate vocalizations by Romain A. Lefèvre, Ciara C.R. Sypherd, Élodie F. Briefer

“…The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) and negative (unpleasant) emotional valence, produced in various contexts by seven species of ungulates. …”
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Time series forecasting of bed occupancy in mental health facilities in India using machine learning by G. Avinash, Hariom Pachori, Avinash Sharma, SukhDev Mishra

“…This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. …”
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Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study by Wei Wu, Zheng-gang Fang, Shu-qin Yang, Cai-xia Lv, Shu-yi An

“…A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.Design Time-series study.Setting The USA was the setting for this study.Main outcome measures Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.Results In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.Conclusions The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.…”
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Study on Finger Gesture Interface Using One-Channel EMG by Hee-Yeong Yang, Young-Shin Han, Choon-Sung Nam

“…Four machine learning models were used: eXtreme Gradient Boost, Random Forest, k-Nearest Neighbors, and Logistic Regression. …”
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Mitigating GNSS Multipath Effects Using XGBoost Integrated Classifier Based on Consistency Checks by Dengao Li, Xiaoli Ma, Jumin Zhao, Fanming Wu

“…To deal with the above problems, this paper proposes a two-layer consistency-checks (CC) positioning model based on eXtreme Gradient Boosting (XGBoost) integrated learner. …”
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Transcending the urban–rural dichotomy: inequality in urban green space availability among urban neighbourhoods, urban villages and rural villages in Guangzhou, China by Fubin Luo, Yunzheng Zhang, Luyang Zhang

“…We first explored the inequality in UGS availability among UN, UV and RV by employing the Gini and Theil indices and then used the eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanation (SHAP) explainer to elucidate the intricate association between neighbourhood socioeconomic statuses and UGS availability from a local perspective. …”
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Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost by Bo Sun, Tuo Sun, Pengpeng Jiao

“…Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. …”
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XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites by Salman Khan, Sumaiya Noor, Tahir Javed, Afshan Naseem, Fahad Aslam, Salman A. AlQahtani, Nijad Ahmad

“…By fusing word embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. …”
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Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information by Binghua Li, Xin Li, Xiaoyu Li, Li Wang, Jun Lu, Jia Wang

“…After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. …”
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Forecasting mental states in schizophrenia using digital phenotyping data. by Thierry Jean, Rose Guay Hottin, Pierre Orban

“…Besides it remains unclear which machine learning algorithm is best suited for forecast tasks, the eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) algorithms being 2  popular choices in digital phenotyping studies. …”
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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

“…All five machine learning models demonstrated strong discrimination on the validation set (AUC: 0.690–0.858). The eXtreme Gradient Boosting (XGBoost) model was the best performer [AUC: 0.858; sensitivity: 0.877; specificity: 0.981; accuracy: 0.841; positive predictive value: 0.629; negative predictive value: 0.851]. …”
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Towards The Future of Crab Farming: The Application Of AI with Yolox And Yolov9 To Detect Crab Larvae by Furqan Zakiyabarsi, Rezty Amalia Aras, Yabes Dwi Nugroho H, Muhammad Muhaimin Nur, Dimas Aditya Alfarizi, Muhammad Ulil Amri

“…Two models, You Only Look Once eXtreme (YOLOX) and YOLOv9, were evaluated for their performance. …”
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Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models by S Srishti, Pratyush Agrawal, Padmavati Kulkarni, Hrishikesh Chandra Gautam, Meenakshi Kushwaha, V. Sreekanth

“…The ML models included (i) Decision Tree, (ii) Random Forest (RF), (iii) eXtreme Gradient Boosting, and (iv) Support Vector Regression (SVR). …”
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Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data by Hengrui Chen, Hong Chen, Zhizhen Liu, Xiaoke Sun, Ruiyu Zhou

“…To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). …”
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